Wang Minghao, Zhou Yibin, Yao Shenjun, Wu Jianping, Zhu Minhui, Dong Linjuan, Wang Dunjia
Key Laboratory of Geographic Information Science, Ministry of Education, Shanghai, China.
School of Geographic Sciences, East China Normal University, Shanghai, China.
Parasit Vectors. 2024 Dec 18;17(1):511. doi: 10.1186/s13071-024-06587-w.
Dengue fever poses a significant global public health concern, necessitating the monitoring of Aedes mosquito population density. These mosquitoes serve as the disease vectors, making their surveillance crucial for dengue prevention. The objective of this study was to address the difficulty associated with identifying and counting mosquito eggs of wild strains during the monitoring of Aedes albopictus (Diptera: Culicidae) density via ovitraps in field surveys.
We constructed a dataset comprising 1729 images of Ae. albopictus mosquito eggs from wild strains and employed the Segment Anything Model to enhance the applicability of the detection model in complex environments. A two-stage Faster Region-based Convolutional Neural Network model was used to establish a detection model for Ae. albopictus mosquito eggs. The identification and counting process involved applying the tile overlapping method, while morphological filtering was employed to remove impurities. The model's performance was evaluated in terms of precision, recall, and F1 score, and counting accuracy was assessed using R-squared and root mean square error (RMSE).
The experimental results revealed the model's remarkable identification capabilities, achieving precision of 0.977, recall of 0.978, and an F1 score of 0.977. The R-squared value between the actual and identified egg counts was 0.997, with an RMSE of 1.742. The average detection time for a single tile was 0.48 s, which was more than 10 times as fast as the human-computer interaction method in counting an entire image.
The model demonstrated excellent performance in recognizing and counting Ae. albopictus mosquito eggs, indicating great application potential. This study offers novel technological support for enhancing vector control effectiveness and public health standards.
登革热是一个重大的全球公共卫生问题，因此有必要监测埃及伊蚊的种群密度。这些蚊子是疾病的传播媒介，对其进行监测对于预防登革热至关重要。本研究的目的是解决在野外调查中通过诱蚊产卵器监测白纹伊蚊（双翅目：蚊科）密度时，识别和计数野生品系蚊卵所面临的困难。
我们构建了一个包含1729张野生品系白纹伊蚊蚊卵图像的数据集，并采用“分割一切模型”来提高检测模型在复杂环境中的适用性。使用基于区域的双阶段更快卷积神经网络模型建立白纹伊蚊蚊卵检测模型。识别和计数过程采用图像分块重叠法，同时采用形态学滤波去除杂质。通过精确率、召回率和F1分数评估模型性能，并使用决定系数和均方根误差（RMSE）评估计数准确性。
实验结果显示该模型具有出色的识别能力，精确率达到0.977，召回率为0.978，F1分数为0.977。实际卵数与识别卵数之间的决定系数值为0.997，RMSE为1.742。单个图像块的平均检测时间为0.48秒，比人机交互方法对整个图像进行计数的速度快10倍以上。
该模型在识别和计数白纹伊蚊蚊卵方面表现出色，具有很大的应用潜力。本研究为提高病媒控制效果和公共卫生水平提供了新的技术支持。
